Software Engineer II (AI | Up to 40LPA)

• Own the end-to-end MLOps lifecycle, from model packaging and CI/CD to deployment, monitoring, and rollback for computer vision, NLP, and multi-modal models.
• Design and maintain scalable training and inference pipelines for large datasets and models, optimizing for cost, latency, and throughput.
• Build and manage containerized deployment infrastructure (Docker, Kubernetes) for hosted deep learning and geoprocessing services.
• Set up and maintain experiment tracking, model registry, and versioning systems to ensure reproducibility across the research-to-production lifecycle.
• Implement model monitoring and observability - drift detection, performance degradation alerts, logging, and dashboards, for models running in production.
• Apply model optimization techniques (quantization, pruning, knowledge distillation) to improve inference efficiency in production.
• Collaborate with Research Engineers, Backend Engineers, and Product teams to translate research ideas into deployable, production-ready services.
• Develop and maintain infrastructure-as-code, monitoring, and logging for all deployed ML/AI software.

• 3+ years of experience in MLOps, ML infrastructure, or applied AI/ML engineering, with exposure to Computer Vision or NLP systems.
• Hands-on experience with workflow orchestration frameworks (preferably Temporal) for building reliable, fault-tolerant, long-running distributed workflows.
• Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, OpenCV, or HuggingFace Transformers.
• Hands-on experience with Docker, Kubernetes, and containerized ML deployment pipelines in production environments.
• Experience building and maintaining CI/CD pipelines for ML systems (e.g., Jenkins, GitHub Actions, GitLab CI).
• Working knowledge of experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, DVC).

This role is for one of our client companies - a VC-backed Construction Technology (ConTech) startup that has raised $49.7M USD in funding.

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